DATA_ROOT=/daidai/kitti_dataset/raw_data/kitti/
TEST_FILE=kitti_eval/test_files_eigen.txt
RESULTS_DIR=results/depth

DISP_NET=/daidai/daidai/SC-SfMLearner/checkpoints/sknet_256/11-23-21:03/dispnet_checkpoint.pth.tar

#  predict depth and save results to "results_dir/predictions.npy"
python test_disp.py --dispnet DispSKNet --img-height 256 --img-width 832 \
--pretrained-dispnet $DISP_NET --dataset-dir $DATA_ROOT --dataset-list $TEST_FILE \
--output-dir $RESULTS_DIR

# evaluate depth using SfMLearner original version (copy from tensorflow codes) for fair comparison
# please use python2.7
#python ./kitti_eval/eval_depth.py --kitti_dir=$DATA_ROOT \
#--test_file_list $TEST_FILE \
#--pred_file=$RESULTS_DIR/predictions.npy